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Getting into the game of AI

For many auditors, using automation and analytics is a first step in their digital journey towards an AI-enabled audit. Below are some ways you can get started with using these technologies and more.

We all work with computers daily and yet most of us do not know how a microchip works nor what's on the motherboard. Likewise, auditors do not need to become experts in algorithms and mathematical theory underlying AI to learn about AI use and tools. If you're interested in exploring the benefits of AI and automation, and want to start implementing this technology into your practice, here are some ways to get into the AI game:

Get informed and educated

There are many great resources available to help you understand what AI is all about. At CPA Canada, we have curated our top materials to help CPAs get the most out of AI, including:

As you read through these publications, you'll see there are many opportunities and benefits to using automation and AI for practitioners in the assurance profession (noted below). Go further with your knowledge and read about AI opportunities and tools that are in the marketplace. Be curious about how other organizations are leveraging AI and if there are similar ideas that can be considered for your organization. Many of these concepts are also discussed at our annual Technology Forum.

Ask your clients how they are implementing robotic process automation (RPA) processes and AI. Auditors are required to understand their clients’ systems, including those situations where the clients have implemented AI into financial reporting processes.

Deepen your knowledge by attending professional development seminars and webinars as a starting point to further your knowledge about AI and related topics such as data analytics or automation. The Robotic Process Automation Certificate  presented jointly by CPA Canada and the American Institute of Public Accountants (AICPA) is another great resource to check out!

Identify AI leaders within your organization

Find out more about your organization’s AI leaders and if there aren’t any, ask “Why not?” Under-standing who in your organization to approach will place you in a better position to support tangible change and implementation of any AI opportunities that you identify. If no internal connections exist, join an external networking group. Ask them what tools they are using and for any advice on getting started in this area.

Identify opportunities for automation

It is important to understand that implementing AI is a journey and not just a one-step process. One of the first steps in implementing AI is identifying opportunities for using automation and analytics. Be strategic. An ideal place to start is with high-benefit, low-effort opportunities. Processes that lend themselves to automation are consistent and repetitive in nature (e.g., reviewing spreadsheets, filtering and sorting information, reviewing documents and manually entering information into systems of record, or following decision-making processes based on facts and circumstances) and can quickly improve efficiencies and quality of work.

Once you’ve digitized your audit processes and obtained schedules and information from clients in electronic format, it will be easier to identify opportunities to use automation and analytics. Our publication, The Data-Driven Audit: How Automation and AI are Changing the Audit and the Role of the Auditor, prepared jointly with the AICPA, includes an appendix with many examples of using automation and AI throughout your audits. These examples will help you identify further opportunities and challenges of using automation and AI in the future.

A few example opportunities for using automation include:

Materiality and scoping

RPA and analytics can be used to extract data from prior periods or interim financial statements to determine proposed materiality based on a range of benchmarks. The same techniques can be utilized to determine materiality in a continuous or real-time audit. RPA and analytics can be applied to identify anomalous transactions or areas that have not followed the understood course of business in order to determine scope and focus testing on accounts or transactions that appear to present a greater risk of misstatement.

Automating procedures

Digitize aspects of the audit and gain efficiencies in automating audit procedures so that “audit bots” can perform repetitive tasks through RPA. For instance, these “audit bots” can:

  • copy data across different audit files without risk of human fatigue or input errors
  • run calculations (typically those that require business rules to be considered, such as simple tax calculations) to assist in determining financial statement mathematical accuracy and internal consistency, as well as tie-outs of prior-year amounts
  • rebuild financial statements from underlying data to form independent expectations of the financial statements for tie-out purposes

Contract review

The time taken to review significant contracts can be greatly reduced by using automation and AI. With regards to automation, optical character recognition (OCR) can be used to extract terms from standard contracts to perform comparisons and ensure no changes have been made (or to evaluate the changes).

Due to evolving accounting standards, leases are a good example of an area that can benefit from large-volume data analysis and extraction of key contract term information. Contract information can be used to substantively test the population as a whole or simply identify riskier areas for targeted review and testing.

Identify opportunities for AI

Once you’ve implemented automation into your audits, you will be able to identify opportunities for using AI as well. For example, tasks that require you to look for patterns in data or check for patterns in high volumes of data that would be challenging or time-consuming for a human to do (e.g., higher than a certain number, beyond a date, percentage, reciprocal reviews, geographical clustering or combinations of any of the aforementioned) are great opportunities for using AI.

Moreover, think about data acquisition and standardizing processes to acquire data from different clients in a consistent format. If data is obtained in a consistent, structured format, client after client, year after year, AI tools may be easier to implement.

Begin implementing AI processes on a small scale first and assess the results using professional judgment. Below are some examples of AI opportunities you can use throughout your audits. Make sure to check out the rest of the publication for more opportunities and challenges that you may encounter as you think about implementing AI.

Understanding the business and risk assessment

Natural Language Processing (NLP) techniques enable an AI tool to review information obtained through RPA techniques on both public and non-public information. NLP techniques could enable the auditor to scan an entity’s annual report, regulatory filings, phone transcripts with investors, websites, articles of association, and meeting minutes, and encapsulate these materials into a coherent summary of the business, its purpose, and risk profile:

  • Building on knowledge of similar clients and clients' industries, the AI could suggest relevant risk criteria; for example, common misstatements identified for specific financial statement line items, analysts' focus areas or accounting developments.
  • AI reviews of board minutes, internal audit reports, significant and unusual transactions, legal matters, market/customer/employee sentiment from emails, customer complaints, news stories, social media, and online chats can all be summarized for the auditor.

Estimates

The assessment of management’s estimates is a key and complex area of any audit – one that requires significant auditor judgment. However, in some cases, management may come up with an estimate for which AI can be used as part of the audit process. Traditional audit techniques used to audit estimates will typically fall into one of three categories (or a combination of the three):

  • performance of management's process
  • retrospective testing
  • development of an independent estimate

An array of automation and AI techniques can be used to perform variations of these techniques.

For example, in estimating the likelihood of non-repayment for a debtor or bad debt provision, management has set a rate at which they believe the likelihood of default is expected. Using machine learning, the audit team could build an independent model to predict this likelihood based on historical bad debt write-offs. Once the model is built, it could be retrained every year based on actual loss data.

This independent estimate could be made for each individual loan (or by portfolio or type of loan), and then compared to the result of management’s estimate. The AI tool could also be trained to incorporate other relevant observable factors, such as:

  • interest rate movements
  • customer credit ratings
  • share price
  • contractual terms
  • housing starts
  • unemployment rates

Inclusion of these factors could also enable determination of an independent expected loss estimate for comparison with the client’s estimate.

While the audit team would still need to perform procedures on the underlying data as well as management’s methodology, a machine-learning model would provide a more comprehensive estimate of the likelihood of default. Information gathered across industries and geographical locations could also provide the auditor with industry information to come up with expected loss provision by customer.

Reporting

An AI tool can compile and analyze the summary of adjusted and unadjusted misstatements, and of aggregated control deficiencies. For example, an AI tool can identify and join adjustments posted and control deficiencies to determine if there are common denominators or specific audit areas that require additional assessment. This assessment could be presented to the auditor for consideration.

Again, these examples are not meant to be a comprehensive list. Rather they are meant to help you begin thinking about how to implement automation and AI and the opportunities available to you in your audits. And, when you’re ready to move forward, discover what AI and automation tools are in the marketplace, make connections to learn from industry leaders, recognize the opportunities for AI and automation, and get the relevant and reliable data you need to start implementing your AI and automation projects.

Next steps

Get additional resources on technology

Curious about other technologies? CPA Canada has additional tech resources to help you that include blockchain, crypto-assets, data analytics, AI, RPA and more. Check out our technology resources page.

Keep the conversation going

Have you begun to implement automation and AI in your processes? What other opportunities have you been able to identify throughout your audits? Post a comment below or email us directly.

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Disclaimer

The views and opinions expressed in this article are those of the author and do not necessarily reflect that of CPA Canada.